Cargando…

Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta‐analysis

BACKGROUND: Machine learning (ML) models are promising tools for predicting adverse postoperative outcomes in cardiac surgery, yet have not translated to routine clinical use. We conducted a systematic review and meta‐analysis to assess the predictive performance of ML approaches. METHODS: We conduc...

Descripción completa

Detalles Bibliográficos
Autores principales:	Penny‐Dimri, Jahan C., Bergmeir, Christoph, Perry, Luke, Hayes, Linley, Bellomo, Rinaldo, Smith, Julian A.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	John Wiley and Sons Inc. 2022
Materias:	Review
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9804388/ https://www.ncbi.nlm.nih.gov/pubmed/36001761 http://dx.doi.org/10.1111/jocs.16842

Ejemplares similares

Tree-based survival analysis improves mortality prediction in cardiac surgery
por: Penny-Dimri, Jahan C., et al.
Publicado: (2023)

Paying attention to cardiac surgical risk: An interpretable machine learning approach using an uncertainty-aware attentive neural network
por: Penny-Dimri, Jahan C., et al.
Publicado: (2023)

Donor Cardiac Troponin for Prognosis of Adverse Outcomes in Cardiac Transplantation Recipients: a Systematic Review and Meta-analysis
por: Liu, Zhengyang, et al.
Publicado: (2021)

The prognostic significance of postoperative hyperbilirubinemia in cardiac surgery: systematic review and meta-analysis
por: Raveendran, Dev, et al.
Publicado: (2022)

The prognostic value of elevated neutrophil–lymphocyte ratio for cardiac surgery‐associated acute kidney injury: A systematic review and meta‐analysis
por: Wheatley, Joseph, et al.
Publicado: (2022)

Elevated Cardiac Troponin to Detect Acute Cellular Rejection After Cardiac Transplantation: A Systematic Review and Meta-Analysis
por: Liu, Zhengyang, et al.
Publicado: (2022)

Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study
por: Nanayakkara, Shane, et al.
Publicado: (2018)

Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery
por: Garcia-Canadilla, Patricia, et al.
Publicado: (2022)

Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients
por: Karri, Roshan, et al.
Publicado: (2022)

Immune checkpoint inhibitors for advanced non-small cell lung cancer: emerging sequencing for new treatment targets
por: Aguiar, Pedro Nazareth, et al.
Publicado: (2017)

Closing the Gap in Surveillance and Audit of Invasive Mold Diseases for Antifungal Stewardship Using Machine Learning
por: Baggio, Diva, et al.
Publicado: (2019)

Urinary Biomarkers may Complement the Cleveland Score for Prediction of Adverse Kidney Events After Cardiac Surgery: A Pilot Study
por: Albert, Christian, et al.
Publicado: (2020)

Predictive Value of Plasma NGAL:Hepcidin-25 for Major Adverse Kidney Events After Cardiac Surgery with Cardiopulmonary Bypass: A Pilot Study
por: Albert, Christian, et al.
Publicado: (2021)

Forecast evaluation for data scientists: common pitfalls and best practices
por: Hewamalage, Hansika, et al.
Publicado: (2022)

Bench-to-bedside review: Inotropic drug therapy after adult cardiac surgery – a systematic literature review
por: Gillies, Michael, et al.
Publicado: (2005)

Transduction of ferret airway epithelia using a pre-treatment and lentiviral gene vector
por: Cmielewski, Patricia, et al.
Publicado: (2014)

Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis
por: Song, Zhe, et al.
Publicado: (2022)

The hemodynamic effects of intravenous paracetamol (acetaminophen) vs normal saline in cardiac surgery patients: A single center placebo controlled randomized study
por: Chiam, Elizabeth, et al.
Publicado: (2018)

Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning
por: Hong, Liang, et al.
Publicado: (2022)

Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach
por: Heilbroner, Samuel Peter, et al.
Publicado: (2021)

Prediction of lactate concentrations after cardiac surgery using machine learning and deep learning approaches
por: Kobayashi, Yuta, et al.
Publicado: (2023)

Decreased mean perfusion pressure as an independent predictor of acute kidney injury after cardiac surgery
por: Hu, Raymond, et al.
Publicado: (2020)

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches
por: Thongprayoon, Charat, et al.
Publicado: (2020)

Prediction of the development of acute kidney injury following cardiac surgery by machine learning
por: Tseng, Po-Yu, et al.
Publicado: (2020)

Machine learning for the prediction of acute kidney injury in patients after cardiac surgery
por: Xue, Xin, et al.
Publicado: (2022)

Machine learning-based prediction model for postoperative delirium in non-cardiac surgery
por: Lee, Dong Yun, et al.
Publicado: (2023)

NGAL/hepcidin-25 ratio and AKI subtypes in patients following cardiac surgery: a prospective observational study
por: Elitok, Saban, et al.
Publicado: (2021)

Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection
por: Liu, Nan, et al.
Publicado: (2014)

Correction: Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach
Publicado: (2021)

Urinary interleukin-18 does not predict acute kidney injury after adult cardiac surgery: a prospective observational cohort study
por: Haase, Michael, et al.
Publicado: (2008)

Sarcopenia Adversely Affects Outcomes following Cardiac Surgery: A Systematic Review and Meta-Analysis
por: Ansaripour, Ali, et al.
Publicado: (2023)

Contrast-enhanced ultrasound to evaluate changes in renal cortical perfusion around cardiac surgery: a pilot study
por: Schneider, Antoine G, et al.
Publicado: (2013)

Cardiac Comorbidity Risk Score: Zero‐Burden Machine Learning to Improve Prediction of Postoperative Major Adverse Cardiac Events in Hip and Knee Arthroplasty
por: Onishchenko, Dmytro, et al.
Publicado: (2022)

Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery
por: Yu, Yue, et al.
Publicado: (2022)

Prediction model for postoperative atrial fibrillation in non-cardiac surgery using machine learning
por: Oh, Ah Ran, et al.
Publicado: (2023)

Prediction model for myocardial injury after non-cardiac surgery using machine learning
por: Oh, Ah Ran, et al.
Publicado: (2023)

Prediction of Acute Kidney Injury After Cardiac Surgery Using Interpretable Machine Learning
por: Ejmalian, Azar, et al.
Publicado: (2022)

Applications of Machine Learning in Cardiac Electrophysiology
por: Muthalaly, Rahul G, et al.
Publicado: (2020)

Machine learning approach identified clusters for patients with low cardiac output syndrome and outcomes after cardiac surgery
por: Zhao, Xu, et al.
Publicado: (2022)

Time series extrinsic regression: Predicting numeric values from time series data
por: Tan, Chang Wei, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_u4j84hgei9res91u1c7o1ddb4n